The following background information may present examples of specific aspects of the prior art (e.g., without limitation, approaches, facts, or common wisdom) that, while expected to be helpful to further educate the reader as to additional aspects of the prior art, is not to be construed as limiting the present invention, or any embodiments thereof, to anything stated or implied therein or inferred thereupon.
The following is an example of a specific aspect in the prior art that, while expected to be helpful to further educate the reader as to additional aspects of the prior art, is not to be construed as limiting the present invention, or any embodiments thereof, to anything stated or implied therein or inferred thereupon. By way of educational background, an aspect of the prior art generally useful to be aware of is that one goal in signal analysis and modeling is to represent the information as efficiently as possible with as few parameters as possible. This is useful for example, in signal detection and classification. Signal coding, which also may be referred to as compression, has a similar objective, which is to minimize the number of parameters, typically represented by bits, being stored or communicated, thus increasing efficiency of storing, distributing, and transmitting the information. The process of transforming a source sequence into a set of model parameters is called encoding and restoring is referred to as decoding. Therefore, the same methods can be applied to either signal modeling or coding. However, a coder is assumed to be used in combination with a second process, a decoder which reconstructs the signal from its coded parameters. Hence, for methodological purposes, coding can be viewed as a technique that encompasses modeling as part of its process.
Typically, in encoding, an input signal is divided into intervals, often called frames, sections, or events. Each frame can be transformed by windowing and/or filtering, and possibly other operations, to obtain a windowed/filtered/transformed frame. Standard oscillator models transform a current data frame into a small set of parameters consisting of delays or pointers and weight coefficients associated with them. The pointers reference fixed-lengths blocks in a buffer containing a restored version of the earlier acquired data frames. The restoration of a frame takes place once its model parameters have been estimated, and the restored frame is kept in memory, creating a sequence of historical data that represents a restored version of the input sequence. The blocks of these historic data are chosen so that their weighted sum provides the ‘best match’ to the current data frame, where ‘best match’ may be defined, in many typical applications, as the one which minimizes the mean squared error between the current frame and its model. In this way, an input signal is replaced by a set of integer address codes pointing to the match locations and the multiplier coefficients associated with weights of the match data blocks.
The following is an example of a specific aspect in the prior art that, while expected to be helpful to further educate the reader as to additional aspects of the prior art, is not to be construed as limiting the present invention, or any embodiments thereof, to anything stated or implied therein or inferred thereupon. By way of educational background, another aspect of the prior art generally useful to be aware of is that one limitation of the classical oscillators, also called self-excited models, for example the self-excited vocoder (SEV), is the delineation they make between the previously modeled frames of data and the current frame being modeled, in that the data in the current frame does not participate in deriving the model parameters for that frame. This approach works well for modeling source patterns that re-occur on a time scale that exceeds at least one frame length. Thus, traditional oscillators may be considered to be methods for modeling long scale structures in data.
The following is an example of a specific aspect in the prior art that, while expected to be helpful to further educate the reader as to additional aspects of the prior art, is not to be construed as limiting the present invention, or any embodiments thereof, to anything stated or implied therein or inferred thereupon. By way of educational background, another aspect of the prior art is that typical modern coders may employ multiple models to encode the different scales in the source patterns. For instance, the Adaptive Multi-Rate (AMR) family of codecs used in mobile telecommunications typically utilize three models in tandem, first a linear predictor (LP) for modeling short scale patterns, followed by an “adaptive codebook” (AC), which is an improved SEV-like model that can encode mid-to-long scale structures, and finally a third model, which encodes the residual remaining after the first two models have been applied. The AC model in AMR improves on the traditional SEV by allowing some limited section of data from the current input frame to be used for modeling that data. This extends the range of structures that one can model with AC to mid-to-long scale structures. However, this improvement still may not allow modeling of all source scales, which is why LP is used prior to AC in AMR.
It is typical when modeling signals in current art, to separate data into different scales or components and model those components individually. An input may be split into frequency bands, wavelets, or other types of waveforms so that these components can be coded separately, generating multiple sets of parameters for each frame. Referring back to speech coding, another example of this is a family of coders called the Multiband Excitation (MBE, IMBE, and AMBE), which divide an input signal into frequency bands, based on voiced/unvoiced characteristics of each band, and encode the individual bands separately.
Coding a single frame in a form of multiple models or components means that the frame is represented by the corresponding multiple sets of coding parameters, each typically assigned a fixed coding budget. Encoding signals with multiple sets of parameters may not be efficient if a comparable modeling quality can be achieved with a smaller, single set of parameters. The need to represent signals efficiently in a small set of parameters in order to extract information, maximize transmission rates, and to minimize memory in storage systems, all motivate development of the more efficient coding technologies.
In view of the foregoing, it is clear that these traditional techniques are not perfect and leave room for more optimal approaches.
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